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Record W2602016067 · doi:10.5555/3021426.3021444

VNE-Sim: A Virtual Network Embedding Simulator

2016· article· en· W2602016067 on OpenAlexaff
Soroush Haeri, Ljiljana Trajković

Bibliographic record

VenueSimulation Tools and Techniques for Communications, Networks and System · 2016
Typearticle
Languageen
FieldComputer Science
TopicSoftware-Defined Networks and 5G
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsComputer scienceScalabilityNetwork virtualizationNetwork topologyVirtualizationEmbeddingDistributed computingThe InternetModular designVirtual networkNetwork simulationComputer networkOperating systemCloud computing

Abstract

fetched live from OpenAlex

Computer network virtualization and virtual network embedding (VNE) algorithms have gained significant attention in recent years. Performance evaluation of VNE algorithms mainly relies on discrete event simulations. However, modular and scalable VNE simulators are not available. In this paper, we describe newly developed simulator VNE-Sim that enables users to define and implement network elements possessing various resources. It also enables researchers to generate various topologies of Internet Service Provider and data center networks for the evaluation of VNE algorithms. Furthermore, VNE-Sim provides the infrastructure for implementing both single and batch request processing approaches.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.034
GPT teacher head0.295
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2016
Admission routes1
Has abstractyes

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